Coactivation of antagonist muscles is readily observed early in motor learning, in interactions with unstable mechanical environments and in motor system pathologies. Here we present evidence that the nervous system uses coactivation control far more extensively and that patterns of cocontraction during movement are closely tied to the specific requirements of the task. We have examined the changes in cocontraction that follow dynamics learning in tasks that are thought to involve finely sculpted feedforward adjustments to motor commands. We find that, even following substantial training, cocontraction varies in a systematic way that depends on both movement direction and the strength of the external load. The proportion of total activity that is due to cocontraction nevertheless remains remarkably constant. Moreover, long after indices of motor learning and electromyographic measures have reached asymptotic levels, cocontraction still accounts for a significant proportion of total muscle activity in all phases of movement and in all load conditions. These results show that even following dynamics learning in predictable and stable environments, cocontraction forms a central part of the means by which the nervous system regulates movement.
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http://dx.doi.org/10.1007/s00221-008-1457-y | DOI Listing |
ACS Appl Energy Mater
January 2025
Advanced Materials Research Group, Faculty of Engineering, University of Nottingham, Nottingham NG7 2RD, United Kingdom.
Magnesium hydride (MgH) is a promising material for solid-state hydrogen storage due to its high gravimetric hydrogen capacity as well as the abundance and low cost of magnesium. The material's limiting factor is the high dehydrogenation temperature (over 300 °C) and sluggish (de)hydrogenation kinetics when no catalyst is present, making it impractical for onboard applications. Catalysts and physical restructuring (e.
View Article and Find Full Text PDFLearn Health Syst
January 2025
Northwell New Hyde Park New York USA.
Introduction: Learning health networks (LHNs) improve clinical outcomes by applying core tenets of continuous quality improvements (QI) to reach community-defined outcomes, data-sharing, and empowered interdisciplinary teams including patients and caregivers. LHNs provide an ideal environment for the rapid adoption of evidence-based guidelines and translation of research and best practices at scale. When an LHN is established, it is critical to understand the needs of all stakeholders.
View Article and Find Full Text PDFSimulating large molecular systems over long timescales requires force fields that are both accurate and efficient. In recent years, E(3) equivariant neural networks have lifted the tension between computational efficiency and accuracy of force fields, but they are still several orders of magnitude more expensive than established molecular mechanics (MM) force fields. Here, we propose Grappa, a machine learning framework to predict MM parameters from the molecular graph, employing a graph attentional neural network and a transformer with symmetry-preserving positional encoding.
View Article and Find Full Text PDFThe Problem: People use social media platforms to chat, search, and share information, express their opinions, and connect with others. But these platforms also facilitate the posting of divisive, harmful, and hateful messages, targeting groups and individuals, based on their race, religion, gender, sexual orientation, or political views. Hate content is not only a problem on the Internet, but also on traditional media, especially in places where the Internet is not widely available or in rural areas.
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